摘要
精准电生理特征融合在脑机交互实现中占据重要地位。本文面向情感计算的重大需求,基于人体脑电信号数据集探索多脑区多情绪的协同关系。研究基于23名健康被试观看视频诱发情绪的14导脑电信号数据库,协同时频分析、功率谱分析与多尺度熵分析,提取五个脑电频段的特征,结合统计显著性检验,对比分析3种情绪大类和9种情绪细类的结果异同。研究发现,随频率增大情绪刺激态熵值逐渐高于基线态熵值,3种情绪大类和9种情绪细类间的功率指标与多尺度熵指标差异普遍显著,可为情绪识别提供重要依据,从而揭示多情绪时频脑电能量与熵规律。
Precise electrophysiological feature fusion plays an important role in Brain-Computer Interaction technology.Geared to the critical demands of affective computing,this paper discusses the synergic effect among multiple encephalic regions and diverse emotions based on datasets of the human electroencephalogram(EEG).Based on the database of 14-leads EEG signals of 23 healthy subjects whose emotions are evoked by watching a video,we reconcile time-frequency analysis,power spectrum analysis,and multi-scale entropy analysis methods to collect multi-dimensional feature indexes from 5 EEG frequency bands for fusion comparison under 3 different emotion categories and 9 subcategories.Then we summarize the classification basis and carry out verification with statistical significance analysis.It is found that the sampling entropy of the stimuli state is gradually higher than that of the baseline state with the increase of frequency,and the indexes of the3 kinds of emotion categories or 9 kinds of emotion subcategories are generally significantly different,which could provide an important basis for emotion recognition,and furthermore to reveal the time-frequency rule of EEG energy and entropy under diverse emotions.
作者
吴骁
孔怡然
梁霄
陈杏梅
樊璐倩
张楚婷
叶建宏
史文彬
WU Xiao;KONG Yiran;LIANG Xiao;CHEN Xingmei;FAN Luqian;ZHANG Chuting;YEH Chien-Hung;SHI Wenbin(School of Information and Electronics,Beijing Institution of Technology,Beijing,100081;School of Life Science,Beijing Institution of Technology,Beijing,100081)
出处
《生命科学仪器》
2023年第3期64-72,共9页
Life Science Instruments
基金
国家自然科学基金项目(62001026、62171028)
国家高层次人才基金项目(3050012222022)
关键词
情感计算
脑电
时频分析
多尺度熵
方差分析
Affective Computing
Time-Frequency Analysis
EEG
Multi-Scale Entropy
ANOVA
